code: def __init__(
      self, logical_operator, weights, biases, activation, device
  ):
      super(MLPModel, self).__init__(logical_operator)
      self.regression = True
      self.weights = torch.nn.ParameterList(
          [
              torch.nn.Parameter(
                  torch.from_numpy(weight.astype("float32")),
                  requires_grad=False,
              )
              for weight in weights
          ]
      )
      self.biases = torch.nn.ParameterList(
          [
              torch.nn.Parameter(
                  torch.from_numpy(bias.astype("float32")),
                  requires_grad=False,
              )
              for bias in biases
          ]
      )
      self.activation = activation
  
file_path: hummingbird/hummingbird/ml/operator_converters/_mlp_implementations.py
imports: import torch
  from ._physical_operator import PhysicalOperator
label_desc: Use image node_id 1 to create a new MLPModel object from inherited base classes: PhysicalOperator, torch.nn with example: obj = MLPModel(logical_operator, weights, biases, activation, device)